Harsh Bhatia is a computer scientist at the Center for Applied Scientific Computing (CASC) where he has made a name for himself in data analysis, scientific visualization, and machine learning. His wide range of projects include applying topological techniques to understand the behavior of lithium ions, generating topological representations of aerodynamics data, and analyzing and visualizing supercomputers’ performance data. Harsh, who is pictured above (at far left) with fellow CASC researchers, states, “A unique aspect of the Lab’s data science community is its versatility. We’re a big team from diverse backgrounds and work on varied problems.”

He explains that the human brain has a natural tendency to detect and retain patterns in everything the eyes observe, and scientific visualization helps the brain absorb information. “In this field, we can develop new techniques that distill extremely complex data into comprehensible visual information,” Harsh adds. “Visualization enriches the process of scientific discovery and fosters profound and unexpected insights.”

Harsh is a co-architect of the Multiscale Machine-Learned Modeling Infrastructure (MuMMI), which uses machine learning to guide multiscale simulations towards configurations that are important for the hypotheses under investigation. He notes, “MuMMI offers a new paradigm that promises to solve the problems no other technology can.” MuMMI is arbitrarily scalable and has been demonstrated to simulate the interaction between RAS proteins and eight kinds of lipids to investigate RAS dynamics on a macroscale as well as on a molecular level. The MuMMI team won the SC19 Best Paper Award for this work. “The recognition is very satisfying for the entire team who worked tirelessly to create this technology,” Harsh says.

In another project, Harsh explores topological analysis of molecular dynamics data and has developed an open-source tool called TopoMS (Topological Analysis for Molecular Systems). The tool provides detailed topological analysis of atomic shapes and volumes as well as atomic bonds in molecular and condensed matter systems such as lithium salt and benzene molecules. The project was featured in the Journal of Computational Chemistry.

“At the Lab, no two problems are the same. Therefore, as a team, researchers deliver hundreds of new data science solutions each year,” Harsh notes. “We are very fortunate to have access to many high-impact projects so we can really make a difference with our research.”

Harsh was a Lawrence Graduate Scholar and an LLNL postdoctoral researcher before joining CASC full time in 2017. He earned his Ph.D. from the University of Utah’s Scientific Computing and Imaging Institute, and his B.Tech. from the Dhirubhai Ambani Institute of Information and Communication Technology in Gandhinagar, India.